This will be follow-up from the Sang lab meeting on 05/21/2020.
Figure 3a
i) pull the granulocyte progenitor population out of granulocytes
ii) split up macrophages and monocytes
iii) prepare image (or supplemental image) with the singleR naming of the cellsFigure 3c
i) changing the heatmap to a bar plotFigure 3d
i) look at different color schemes to make the contrast more starkDEG Work
i) provide MK DEG list
ii) provide violin plots for all MK DEGs
iii) change the color of the violin plots (blue = Migr, red = Mpl)
iv) run DE analysis on other clustersIncluding the granulocyte progenitor population, and splitting up the monocyte and macrophage populations.
## Granulocyte1 Granulocyte2 Granulocyte3 B cell MK
## "Granulocyte" "Granulocyte" "Granulocyte" "B-cell" "MK"
## Granulocyte4 Stem Cell Monocyte Macrophage Erythroid
## "Granulocyte" "HSPC" "Monocyte" "Macrophage" "Erythroid"
## B-cell pro T-cell/NK MEP
## "B-cell" "T-cell/NK" "MEP"
## [1] "Granulocyte" "B-cell" "MK" "HSPC" "Monocyte"
## [6] "Macrophage" "Erythroid" "T-cell/NK" "MEP"
Looking at creating bar plots for the figures instead of heatmap
Another version of a potential bar chart
## Cell Type Condition Count Percentage Condition2
## 1 Granulocyte Control 895 34 Control
## 2 B-cell Control 946 36 Control
## 3 MK Control 21 1 Control
## 4 HSPC Control 127 5 Control
## 5 Monocyte Control 192 7 Control
## 6 Macrophage Control 156 6 Control
## [1] "MEP" "T-cell/NK" "Erythroid" "Macrophage" "Monocyte"
## [6] "HSPC" "MK" "B-cell" "Granulocyte"
Adding labels to the heatmap
## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
Creating a heatmap to see how SingleR labeled the individual cells in each cluster
Visualizing the data with a bar plots instead of heat plots
Here are all the violin plots for all the differentially expressed genes in the MK cluster.
## Gene P.value Avg.Log.FC Control.Pct Mipl.Pct Pct..Diff Adj..P.value
## 1 Mpo 6.04e-19 1.73 0.476 0.298 0.18 1.87e-14
## 2 Csrp3 2.42e-29 1.63 0.905 0.290 0.62 7.53e-25
## 3 Nedd4 1.17e-27 1.43 1.000 0.775 0.23 3.64e-23
## 4 Hmgb2 1.19e-13 1.41 1.000 0.923 0.08 3.69e-09
## 5 Stmn1 8.60e-08 1.39 0.524 0.273 0.25 2.67e-03
## 6 Ms4a3 2.77e-14 1.34 0.476 0.138 0.34 8.59e-10
## [1] 123
## The default behaviour of split.by has changed.
## Separate violin plots are now plotted side-by-side.
## To restore the old behaviour of a single split violin,
## set split.plot = TRUE.
##
## This message will be shown once per session.
Taking a look at the list of profibrotic factors provided by Priya.
## Mode FALSE TRUE
## logical 18 43
## [1] "Profibrotic factors that were not found in our analysis"
## [1] "Il1" "Il8" "Il12" "Tnfa" "Gmcsf" "Gcsf"
## [7] "Pdgf" "Tsp1" "Tsp" "Coliv" "Col4" "Fn"
## [13] "Cxcl4" "Cscl7" "Fgf" "Vegf" "Tsp1" "C20orf194"
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Pdgfd.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Ctgf.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Col3a1.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Mmp3.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Timp4.
## Warning in SingleExIPlot(type = type, data = data[, x, drop = FALSE], idents =
## idents, : All cells have the same value of Bmp6.